 This paper compares several statistical methods for predicting electricity load, analyzes the results over time, and evaluates the performance of an artificial neural network, ANN, model with genetic algorithms, GA, and adaptive neuro-fuzzy inference systems, ANFIS. The authors use hourly data divided into annual samples for testing and training, and find that the ANNGA model has the best accuracy and lowest error rate when compared to other models. This study demonstrates the importance of understanding the relationship between various factors and electrical load, and provides valuable insight into how to accurately predict electricity usage. This article was authored by Ahmed Mazen Majid al-Kasi, Altaq Bhaskar, and Yavuz Atash.